CredibleDFGO: New Method Improves GPS Positioning Accuracy and Reliability in Urban Areas
Researchers have proposed CredibleDFGO (CDFGO), a differentiable factor graph optimization framework that explicitly trains GNSS positioning systems to produce more reliable uncertainty estimates alongside position fixes. Existing methods improve position accuracy but leave reported covariances poorly calibrated, a problem that worsens in dense urban areas where satellite signals are obstructed. Better-calibrated covariances matter because downstream systems—such as autonomous vehicles or sensor fusion pipelines—rely on these uncertainty estimates to make safe decisions.
Global Navigation Satellite System (GNSS) positioning degrades significantly in urban canyons, where buildings reflect and obstruct satellite signals, and existing differentiable factor graph optimization (DFGO) methods address position accuracy but neglect the reliability of the reported covariance. CredibleDFGO introduces a Weighting Generation Network (WGN) that predicts per-satellite reliability weights, feeding into a differentiable Gauss-Newton solver that outputs both a position estimate and a Hessian-derived posterior covariance. The framework supervises the full East-North predictive distribution end-to-end using proper scoring rules—specifically negative log-likelihood (NLL), the energy score (ES), and a combination of both. Evaluated on three scenes from the UrbanNav dataset, CDFGO achieves consistent improvements in covariance credibility across all test conditions. In the challenging Mong Kok harsh-urban scene, the combined model reduces mean horizontal error from 13.77 m to 11.68 m, NLL from 40.63 to 6.59, and ES from 12.31 to 9.05 compared to the DFGO baseline. Case studies attribute these gains to better axis-wise consistency, more accurately shaped local covariance ellipses, and improved satellite-level reweighting. The paper has been submitted to NAVIGATION: Journal of the Institute of Navigation and is currently available as a preprint on arXiv.
What's missing
The study is evaluated solely on the UrbanNav dataset collected in Hong Kong; generalizability to other urban environments, GNSS constellations, or hardware configurations is not demonstrated. The paper does not report computational overhead of the WGN relative to baseline DFGO, which is relevant for real-time deployment. Long-term or cross-session robustness and performance under varying weather or multipath conditions are not assessed. As a preprint, the work has not yet completed peer review.
What different sources said
- arXiv cs.AICenter
CredibleDFGO: Differentiable Factor Graph Optimization with Credibility Supervision
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